Point Cloud Library (PCL)  1.10.0-dev
brisk_2d.hpp
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39 
40 #ifndef PCL_FEATURES_IMPL_BRISK_2D_HPP_
41 #define PCL_FEATURES_IMPL_BRISK_2D_HPP_
42 
43 ///////////////////////////////////////////////////////////////////////////////////////////
44 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT>
46  : rotation_invariance_enabled_ (true)
47  , scale_invariance_enabled_ (true)
48  , pattern_scale_ (1.0f)
49  , input_cloud_ (), keypoints_ (), scale_range_ (), pattern_points_ (), points_ ()
50  , n_rot_ (1024), scale_list_ (nullptr), size_list_ (nullptr)
51  , scales_ (64)
52  , scalerange_ (30)
53  , basic_size_ (12.0)
54  , strings_ (0), d_max_ (0.0f), d_min_ (0.0f), short_pairs_ (), long_pairs_ ()
55  , no_short_pairs_ (0), no_long_pairs_ (0)
56  , intensity_ ()
57  , name_ ("BRISK2Destimation")
58 {
59  // Since we do not assume pattern_scale_ should be changed by the user, we
60  // can initialize the kernel in the constructor
61  std::vector<float> r_list;
62  std::vector<int> n_list;
63 
64  // this is the standard pattern found to be suitable also
65  r_list.resize (5);
66  n_list.resize (5);
67  const float f = 0.85f * pattern_scale_;
68 
69  r_list[0] = f * 0.0f;
70  r_list[1] = f * 2.9f;
71  r_list[2] = f * 4.9f;
72  r_list[3] = f * 7.4f;
73  r_list[4] = f * 10.8f;
74 
75  n_list[0] = 1;
76  n_list[1] = 10;
77  n_list[2] = 14;
78  n_list[3] = 15;
79  n_list[4] = 20;
80 
81  generateKernel (r_list, n_list, 5.85f * pattern_scale_, 8.2f * pattern_scale_);
82 }
83 
84 ///////////////////////////////////////////////////////////////////////////////////////////
85 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT>
87 {
88  if (pattern_points_) delete [] pattern_points_;
89  if (short_pairs_) delete [] short_pairs_;
90  if (long_pairs_) delete [] long_pairs_;
91  if (scale_list_) delete [] scale_list_;
92  if (size_list_) delete [] size_list_;
93 }
94 
95 ///////////////////////////////////////////////////////////////////////////////////////////
96 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT> void
98  std::vector<float> &radius_list,
99  std::vector<int> &number_list, float d_max, float d_min,
100  std::vector<int> index_change)
101 {
102  d_max_ = d_max;
103  d_min_ = d_min;
104 
105  // get the total number of points
106  const auto rings = radius_list.size ();
107  assert (radius_list.size () != 0 && radius_list.size () == number_list.size ());
108  points_ = 0; // remember the total number of points
109  for (const auto number: number_list)
110  points_ += number;
111 
112  // set up the patterns
113  pattern_points_ = new BriskPatternPoint[points_*scales_*n_rot_];
114  BriskPatternPoint* pattern_iterator = pattern_points_;
115 
116  // define the scale discretization:
117  static const float lb_scale = std::log (scalerange_) / std::log (2.0);
118  static const float lb_scale_step = lb_scale / (float (scales_));
119 
120  scale_list_ = new float[scales_];
121  size_list_ = new unsigned int[scales_];
122 
123  const float sigma_scale = 1.3f;
124 
125  for (unsigned int scale = 0; scale < scales_; ++scale)
126  {
127  scale_list_[scale] = static_cast<float> (pow (double (2.0), static_cast<double> (float (scale) * lb_scale_step)));
128  size_list_[scale] = 0;
129 
130  // generate the pattern points look-up
131  double alpha, theta;
132  for (std::size_t rot = 0; rot < n_rot_; ++rot)
133  {
134  // this is the rotation of the feature
135  theta = double (rot) * 2 * M_PI / double (n_rot_);
136  for (int ring = 0; ring < rings; ++ring)
137  {
138  for (int num = 0; num < number_list[ring]; ++num)
139  {
140  // the actual coordinates on the circle
141  alpha = double (num) * 2 * M_PI / double (number_list[ring]);
142 
143  // feature rotation plus angle of the point
144  pattern_iterator->x = scale_list_[scale] * radius_list[ring] * static_cast<float> (std::cos (alpha + theta));
145  pattern_iterator->y = scale_list_[scale] * radius_list[ring] * static_cast<float> (sin (alpha + theta));
146  // and the gaussian kernel sigma
147  if (ring == 0)
148  pattern_iterator->sigma = sigma_scale * scale_list_[scale] * 0.5f;
149  else
150  pattern_iterator->sigma = static_cast<float> (sigma_scale * scale_list_[scale] * (double (radius_list[ring])) * sin (M_PI / double (number_list[ring])));
151 
152  // adapt the sizeList if necessary
153  const unsigned int size = static_cast<unsigned int> (std::ceil (((scale_list_[scale] * radius_list[ring]) + pattern_iterator->sigma)) + 1);
154 
155  if (size_list_[scale] < size)
156  size_list_[scale] = size;
157 
158  // increment the iterator
159  ++pattern_iterator;
160  }
161  }
162  }
163  }
164 
165  // now also generate pairings
166  short_pairs_ = new BriskShortPair[points_ * (points_ - 1) / 2];
167  long_pairs_ = new BriskLongPair[points_ * (points_ - 1) / 2];
168  no_short_pairs_ = 0;
169  no_long_pairs_ = 0;
170 
171  // fill index_change with 0..n if empty
172  if (index_change.empty ())
173  {
174  index_change.resize (points_ * (points_ - 1) / 2);
175  }
176  std::iota(index_change.begin (), index_change.end (), 0);
177 
178  const float d_min_sq = d_min_ * d_min_;
179  const float d_max_sq = d_max_ * d_max_;
180  for (unsigned int i = 1; i < points_; i++)
181  {
182  for (unsigned int j = 0; j < i; j++)
183  { //(find all the pairs)
184  // point pair distance:
185  const float dx = pattern_points_[j].x - pattern_points_[i].x;
186  const float dy = pattern_points_[j].y - pattern_points_[i].y;
187  const float norm_sq = (dx*dx+dy*dy);
188  if (norm_sq > d_min_sq)
189  {
190  // save to long pairs
191  BriskLongPair& longPair = long_pairs_[no_long_pairs_];
192  longPair.weighted_dx = int ((dx / (norm_sq)) * 2048.0 + 0.5);
193  longPair.weighted_dy = int ((dy / (norm_sq)) * 2048.0 + 0.5);
194  longPair.i = i;
195  longPair.j = j;
196  ++no_long_pairs_;
197  }
198  else if (norm_sq < d_max_sq)
199  {
200  // save to short pairs
201  assert (no_short_pairs_ < index_change.size ()); // make sure the user passes something sensible
202  BriskShortPair& shortPair = short_pairs_[index_change[no_short_pairs_]];
203  shortPair.j = j;
204  shortPair.i = i;
205  ++no_short_pairs_;
206  }
207  }
208  }
209 
210  // no bits:
211  strings_ = int (std::ceil ((float (no_short_pairs_)) / 128.0)) * 4 * 4;
212 }
213 
214 ///////////////////////////////////////////////////////////////////////////////////////////
215 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT> inline int
217  const std::vector<unsigned char> &image,
218  int image_width, int,
219  //const Stefan& integral,
220  const std::vector<int> &integral_image,
221  const float key_x, const float key_y, const unsigned int scale,
222  const unsigned int rot, const unsigned int point) const
223 {
224  // get the float position
225  const BriskPatternPoint& brisk_point = pattern_points_[scale * n_rot_*points_ + rot * points_ + point];
226  const float xf = brisk_point.x + key_x;
227  const float yf = brisk_point.y + key_y;
228  const int x = int (xf);
229  const int y = int (yf);
230  const int& imagecols = image_width;
231 
232  // get the sigma:
233  const float sigma_half = brisk_point.sigma;
234  const float area = 4.0f * sigma_half * sigma_half;
235 
236  // Get the point step
237 
238  // calculate output:
239  int ret_val;
240  if (sigma_half < 0.5)
241  {
242  // interpolation multipliers:
243  const int r_x = static_cast<int> ((xf - float (x)) * 1024);
244  const int r_y = static_cast<int> ((yf - float (y)) * 1024);
245  const int r_x_1 = (1024 - r_x);
246  const int r_y_1 = (1024 - r_y);
247 
248  //+const unsigned char* ptr = static_cast<const unsigned char*> (&image.points[0].r) + x + y * imagecols;
249  const unsigned char* ptr = static_cast<const unsigned char*>(&image[0]) + x + y * imagecols;
250 
251  // just interpolate:
252  ret_val = (r_x_1 * r_y_1 * int (*ptr));
253 
254  //+ptr += sizeof (PointInT);
255  ptr++;
256 
257  ret_val += (r_x * r_y_1 * int (*ptr));
258 
259  //+ptr += (imagecols * sizeof (PointInT));
260  ptr += imagecols;
261 
262  ret_val += (r_x * r_y * int (*ptr));
263 
264  //+ptr -= sizeof (PointInT);
265  ptr--;
266 
267  ret_val += (r_x_1 * r_y * int (*ptr));
268  return (ret_val + 512) / 1024;
269  }
270 
271  // this is the standard case (simple, not speed optimized yet):
272 
273  // scaling:
274  const int scaling = static_cast<int> (4194304.0f / area);
275  const int scaling2 = static_cast<int> (float (scaling) * area / 1024.0f);
276 
277  // the integral image is larger:
278  const int integralcols = imagecols + 1;
279 
280  // calculate borders
281  const float x_1 = xf - sigma_half;
282  const float x1 = xf + sigma_half;
283  const float y_1 = yf - sigma_half;
284  const float y1 = yf + sigma_half;
285 
286  const int x_left = int (x_1 + 0.5);
287  const int y_top = int (y_1 + 0.5);
288  const int x_right = int (x1 + 0.5);
289  const int y_bottom = int (y1 + 0.5);
290 
291  // overlap area - multiplication factors:
292  const float r_x_1 = float (x_left) - x_1 + 0.5f;
293  const float r_y_1 = float (y_top) - y_1 + 0.5f;
294  const float r_x1 = x1 - float (x_right) + 0.5f;
295  const float r_y1 = y1 - float (y_bottom) + 0.5f;
296  const int dx = x_right - x_left - 1;
297  const int dy = y_bottom - y_top - 1;
298  const int A = static_cast<int> ((r_x_1 * r_y_1) * float (scaling));
299  const int B = static_cast<int> ((r_x1 * r_y_1) * float (scaling));
300  const int C = static_cast<int> ((r_x1 * r_y1) * float (scaling));
301  const int D = static_cast<int> ((r_x_1 * r_y1) * float (scaling));
302  const int r_x_1_i = static_cast<int> (r_x_1 * float (scaling));
303  const int r_y_1_i = static_cast<int> (r_y_1 * float (scaling));
304  const int r_x1_i = static_cast<int> (r_x1 * float (scaling));
305  const int r_y1_i = static_cast<int> (r_y1 * float (scaling));
306 
307  if (dx + dy > 2)
308  {
309  // now the calculation:
310 
311  //+const unsigned char* ptr = static_cast<const unsigned char*> (&image.points[0].r) + x_left + imagecols * y_top;
312  const unsigned char* ptr = static_cast<const unsigned char*>(&image[0]) + x_left + imagecols * y_top;
313 
314  // first the corners:
315  ret_val = A * int (*ptr);
316 
317  //+ptr += (dx + 1) * sizeof (PointInT);
318  ptr += dx + 1;
319 
320  ret_val += B * int (*ptr);
321 
322  //+ptr += (dy * imagecols + 1) * sizeof (PointInT);
323  ptr += dy * imagecols + 1;
324 
325  ret_val += C * int (*ptr);
326 
327  //+ptr -= (dx + 1) * sizeof (PointInT);
328  ptr -= dx + 1;
329 
330  ret_val += D * int (*ptr);
331 
332  // next the edges:
333  //+int* ptr_integral;// = static_cast<int*> (integral.data) + x_left + integralcols * y_top + 1;
334  const int* ptr_integral = static_cast<const int*> (&integral_image[0]) + x_left + integralcols * y_top + 1;
335 
336  // find a simple path through the different surface corners
337  const int tmp1 = (*ptr_integral);
338  ptr_integral += dx;
339  const int tmp2 = (*ptr_integral);
340  ptr_integral += integralcols;
341  const int tmp3 = (*ptr_integral);
342  ptr_integral++;
343  const int tmp4 = (*ptr_integral);
344  ptr_integral += dy * integralcols;
345  const int tmp5 = (*ptr_integral);
346  ptr_integral--;
347  const int tmp6 = (*ptr_integral);
348  ptr_integral += integralcols;
349  const int tmp7 = (*ptr_integral);
350  ptr_integral -= dx;
351  const int tmp8 = (*ptr_integral);
352  ptr_integral -= integralcols;
353  const int tmp9 = (*ptr_integral);
354  ptr_integral--;
355  const int tmp10 = (*ptr_integral);
356  ptr_integral -= dy * integralcols;
357  const int tmp11 = (*ptr_integral);
358  ptr_integral++;
359  const int tmp12 = (*ptr_integral);
360 
361  // assign the weighted surface integrals:
362  const int upper = (tmp3 -tmp2 +tmp1 -tmp12) * r_y_1_i;
363  const int middle = (tmp6 -tmp3 +tmp12 -tmp9) * scaling;
364  const int left = (tmp9 -tmp12 +tmp11 -tmp10) * r_x_1_i;
365  const int right = (tmp5 -tmp4 +tmp3 -tmp6) * r_x1_i;
366  const int bottom = (tmp7 -tmp6 +tmp9 -tmp8) * r_y1_i;
367 
368  return (ret_val + upper + middle + left + right + bottom + scaling2 / 2) / scaling2;
369  }
370 
371  // now the calculation:
372 
373  //const unsigned char* ptr = static_cast<const unsigned char*> (&image.points[0].r) + x_left + imagecols * y_top;
374  const unsigned char* ptr = static_cast<const unsigned char*>(&image[0]) + x_left + imagecols * y_top;
375 
376  // first row:
377  ret_val = A * int (*ptr);
378 
379  //+ptr += sizeof (PointInT);
380  ptr++;
381 
382  //+const unsigned char* end1 = ptr + (dx * sizeof (PointInT));
383  const unsigned char* end1 = ptr + dx;
384 
385  //+for (; ptr < end1; ptr += sizeof (PointInT))
386  for (; ptr < end1; ptr++)
387  ret_val += r_y_1_i * int (*ptr);
388  ret_val += B * int (*ptr);
389 
390  // middle ones:
391  //+ptr += (imagecols - dx - 1) * sizeof (PointInT);
392  ptr += imagecols - dx - 1;
393 
394  //+const unsigned char* end_j = ptr + (dy * imagecols) * sizeof (PointInT);
395  const unsigned char* end_j = ptr + dy * imagecols;
396 
397  //+for (; ptr < end_j; ptr += (imagecols - dx - 1) * sizeof (PointInT))
398  for (; ptr < end_j; ptr += imagecols - dx - 1)
399  {
400  ret_val += r_x_1_i * int (*ptr);
401 
402  //+ptr += sizeof (PointInT);
403  ptr++;
404 
405  //+const unsigned char* end2 = ptr + (dx * sizeof (PointInT));
406  const unsigned char* end2 = ptr + dx;
407 
408  //+for (; ptr < end2; ptr += sizeof (PointInT))
409  for (; ptr < end2; ptr++)
410  ret_val += int (*ptr) * scaling;
411 
412  ret_val += r_x1_i * int (*ptr);
413  }
414  // last row:
415  ret_val += D * int (*ptr);
416 
417  //+ptr += sizeof (PointInT);
418  ptr++;
419 
420  //+const unsigned char* end3 = ptr + (dx * sizeof (PointInT));
421  const unsigned char* end3 = ptr + dx;
422 
423  //+for (; ptr<end3; ptr += sizeof (PointInT))
424  for (; ptr<end3; ptr++)
425  ret_val += r_y1_i * int (*ptr);
426 
427  ret_val += C * int (*ptr);
428 
429  return (ret_val + scaling2 / 2) / scaling2;
430 }
431 
432 
433 //////////////////////////////////////////////////////////////////////////////
434 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT> bool
436  const float min_x, const float min_y,
437  const float max_x, const float max_y, const KeypointT& pt)
438 {
439  return ((pt.x < min_x) || (pt.x >= max_x) || (pt.y < min_y) || (pt.y >= max_y));
440 }
441 
442 ///////////////////////////////////////////////////////////////////////////////////////////
443 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT> void
445  PointCloudOutT &output)
446 {
447  if (!input_cloud_->isOrganized ())
448  {
449  PCL_ERROR ("[pcl::%s::initCompute] %s doesn't support non organized clouds!\n", name_.c_str ());
450  return;
451  }
452 
453  // image size
454  const int width = int (input_cloud_->width);
455  const int height = int (input_cloud_->height);
456 
457  // destination for intensity data; will be forwarded to BRISK
458  std::vector<unsigned char> image_data (width*height);
459 
460  for (std::size_t i = 0; i < image_data.size (); ++i)
461  image_data[i] = static_cast<unsigned char> (intensity_ ((*input_cloud_)[i]));
462 
463  // Remove keypoints very close to the border
464  std::size_t ksize = keypoints_->points.size ();
465  std::vector<int> kscales; // remember the scale per keypoint
466  kscales.resize (ksize);
467 
468  // initialize constants
469  static const float lb_scalerange = std::log2 (scalerange_);
470 
471  typename std::vector<KeypointT, Eigen::aligned_allocator<KeypointT> >::iterator beginning = keypoints_->points.begin ();
472  std::vector<int>::iterator beginningkscales = kscales.begin ();
473 
474  static const float basic_size_06 = basic_size_ * 0.6f;
475  unsigned int basicscale = 0;
476 
477  if (!scale_invariance_enabled_)
478  basicscale = std::max (static_cast<int> (float (scales_) / lb_scalerange * (std::log2 (1.45f * basic_size_ / (basic_size_06))) + 0.5f), 0);
479 
480  for (std::size_t k = 0; k < ksize; k++)
481  {
482  unsigned int scale;
483  if (scale_invariance_enabled_)
484  {
485  scale = std::max (static_cast<int> (float (scales_) / lb_scalerange * (std::log2 (keypoints_->points[k].size / (basic_size_06))) + 0.5f), 0);
486  // saturate
487  if (scale >= scales_) scale = scales_ - 1;
488  kscales[k] = scale;
489  }
490  else
491  {
492  scale = basicscale;
493  kscales[k] = scale;
494  }
495 
496  const int border = size_list_[scale];
497  const int border_x = width - border;
498  const int border_y = height - border;
499 
500  if (RoiPredicate (float (border), float (border), float (border_x), float (border_y), keypoints_->points[k]))
501  {
502  //std::cerr << "remove keypoint" << std::endl;
503  keypoints_->points.erase (beginning + k);
504  kscales.erase (beginningkscales + k);
505  if (k == 0)
506  {
507  beginning = keypoints_->points.begin ();
508  beginningkscales = kscales.begin ();
509  }
510  ksize--;
511  k--;
512  }
513  }
514 
515  keypoints_->width = std::uint32_t (keypoints_->size ());
516  keypoints_->height = 1;
517 
518  // first, calculate the integral image over the whole image:
519  // current integral image
520  std::vector<int> integral ((width+1)*(height+1), 0); // the integral image
521 
522  for (std::size_t row_index = 1; row_index < height; ++row_index)
523  {
524  for (std::size_t col_index = 1; col_index < width; ++col_index)
525  {
526  const std::size_t index = row_index*width+col_index;
527  const std::size_t index2 = (row_index)*(width+1)+(col_index);
528 
529  integral[index2] = static_cast<int> (image_data[index])
530  - integral[index2-1-(width+1)]
531  + integral[index2-(width+1)]
532  + integral[index2-1];
533  }
534  }
535 
536  int* values = new int[points_]; // for temporary use
537 
538  // resize the descriptors:
539  //output = zeros (ksize, strings_);
540 
541  // now do the extraction for all keypoints:
542 
543  // temporary variables containing gray values at sample points:
544  int t1;
545  int t2;
546 
547  // the feature orientation
548  int direction0;
549  int direction1;
550 
551  output.resize (ksize);
552  //output.width = ksize;
553  //output.height = 1;
554  for (std::size_t k = 0; k < ksize; k++)
555  {
556  unsigned char* ptr = &output.points[k].descriptor[0];
557 
558  int theta;
559  KeypointT &kp = keypoints_->points[k];
560  const int& scale = kscales[k];
561  int shifter = 0;
562  int* pvalues = values;
563  const float& x = float (kp.x);
564  const float& y = float (kp.y);
565  if (true) // kp.angle==-1
566  {
567  if (!rotation_invariance_enabled_)
568  // don't compute the gradient direction, just assign a rotation of 0 degree
569  theta = 0;
570  else
571  {
572  // get the gray values in the unrotated pattern
573  for (unsigned int i = 0; i < points_; i++)
574  *(pvalues++) = smoothedIntensity (image_data, width, height, integral, x, y, scale, 0, i);
575 
576  direction0 = 0;
577  direction1 = 0;
578  // now iterate through the long pairings
579  const BriskLongPair* max = long_pairs_ + no_long_pairs_;
580 
581  for (BriskLongPair* iter = long_pairs_; iter < max; ++iter)
582  {
583  t1 = *(values + iter->i);
584  t2 = *(values + iter->j);
585  const int delta_t = (t1 - t2);
586 
587  // update the direction:
588  const int tmp0 = delta_t * (iter->weighted_dx) / 1024;
589  const int tmp1 = delta_t * (iter->weighted_dy) / 1024;
590  direction0 += tmp0;
591  direction1 += tmp1;
592  }
593  kp.angle = std::atan2 (float (direction1), float (direction0)) / float (M_PI) * 180.0f;
594  theta = static_cast<int> ((float (n_rot_) * kp.angle) / (360.0f) + 0.5f);
595  if (theta < 0)
596  theta += n_rot_;
597  if (theta >= int (n_rot_))
598  theta -= n_rot_;
599  }
600  }
601  else
602  {
603  // figure out the direction:
604  //int theta=rotationInvariance*round((_n_rot*std::atan2(direction.at<int>(0,0),direction.at<int>(1,0)))/(2*M_PI));
605  if (!rotation_invariance_enabled_)
606  theta = 0;
607  else
608  {
609  theta = static_cast<int> (n_rot_ * (kp.angle / (360.0)) + 0.5);
610  if (theta < 0)
611  theta += n_rot_;
612  if (theta >= int (n_rot_))
613  theta -= n_rot_;
614  }
615  }
616 
617  // now also extract the stuff for the actual direction:
618  // let us compute the smoothed values
619  shifter = 0;
620 
621  //unsigned int mean=0;
622  pvalues = values;
623  // get the gray values in the rotated pattern
624  for (unsigned int i = 0; i < points_; i++)
625  *(pvalues++) = smoothedIntensity (image_data, width, height, integral, x, y, scale, theta, i);
626 
627 #ifdef __GNUC__
628  using UINT32_ALIAS = std::uint32_t;
629 #endif
630 #ifdef _MSC_VER
631  // Todo: find the equivalent to may_alias
632  #define UCHAR_ALIAS std::uint32_t //__declspec(noalias)
633  #define UINT32_ALIAS std::uint32_t //__declspec(noalias)
634 #endif
635 
636  // now iterate through all the pairings
637  UINT32_ALIAS* ptr2 = reinterpret_cast<UINT32_ALIAS*> (ptr);
638  const BriskShortPair* max = short_pairs_ + no_short_pairs_;
639 
640  for (BriskShortPair* iter = short_pairs_; iter < max; ++iter)
641  {
642  t1 = *(values + iter->i);
643  t2 = *(values + iter->j);
644 
645  if (t1 > t2)
646  *ptr2 |= ((1) << shifter);
647 
648  // else already initialized with zero
649  // take care of the iterators:
650  ++shifter;
651 
652  if (shifter == 32)
653  {
654  shifter = 0;
655  ++ptr2;
656  }
657  }
658 
659  //ptr += strings_;
660 
661  //// Account for the scale + orientation;
662  //ptr += sizeof (output.points[0].scale);
663  //ptr += sizeof (output.points[0].orientation);
664  }
665 
666  // we do not change the denseness
667  output.width = int (output.points.size ());
668  output.height = 1;
669  output.is_dense = true;
670 
671  // clean-up
672  delete [] values;
673 }
674 
675 
676 #endif //#ifndef PCL_FEATURES_IMPL_BRISK_2D_HPP_
677 
void compute(PointCloudOutT &output)
Computes the descriptors for the previously specified points and input data.
Definition: brisk_2d.hpp:444
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:397
void resize(std::size_t n)
Resize the cloud.
Definition: point_cloud.h:442
Implementation of the BRISK-descriptor, based on the original code and paper reference by...
Definition: brisk_2d.h:67
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:400
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:402
virtual ~BRISK2DEstimation()
Destructor.
Definition: brisk_2d.hpp:86
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields)...
Definition: point_cloud.h:405
BRISK2DEstimation()
Constructor.
Definition: brisk_2d.hpp:45
void generateKernel(std::vector< float > &radius_list, std::vector< int > &number_list, float d_max=5.85f, float d_min=8.2f, std::vector< int > index_change=std::vector< int >())
Call this to generate the kernel: circle of radius r (pixels), with n points; short pairings with dMa...
Definition: brisk_2d.hpp:97
Definition: norms.h:54
int smoothedIntensity(const std::vector< unsigned char > &image, int image_width, int image_height, const std::vector< int > &integral_image, const float key_x, const float key_y, const unsigned int scale, const unsigned int rot, const unsigned int point) const
Compute the smoothed intensity for a given x/y position in the image.
Definition: brisk_2d.hpp:216